12‐month primary patency rates of contemporary endovascular device therapy for femoro‐popliteal occlusive disease in 6,024 patients: Beyond balloon angioplasty
Bibliographic record
Abstract
BACKGROUND: Endovascular approach to superficial femoral artery (SFA) disease, the most common cause of symptomatic peripheral arterial disease, remains fraught with high failure rates. Newer devices including second-generation nitinol stents, drug-coated stents, drug-coated balloons, covered stents, cryo-therapy, LASER, and directional atherectomy have shown promising results. Clinical equipoise still persists regarding the optimal selection of devices, largely attributable to the different inclusion criteria, study population, length of lesions treated, definition of "patency" and "restenosis," and follow-up methods in the pivotal trials. METHODS: A prospective protocol was developed. We performed a literature search using PubMed from January 2006 to November 2013. Published articles including endovascular interventions in SFA or popliteal arteries with reported 12-month "primary patency" or "binary restenosis" rates as endpoints were included. RESULTS: We identified 6,024 patients in 61 trials reporting 12-month primary patency rates in patients with femoropoliteal disease. Primary patency rates were (weighted average) 77.2% for nitinol stents, 68.8% for covered stents, 84% for drug eluting stents, 78.2% for drug eluting/coated balloon, 60.7% for cryoballoon, 51.1% for LASER atherectomy, 63.5% for directional atherectomy and 70.2% with a combination of endovascular devices. CONCLUSION: The most frequently used endovascular devices yielded various 12-month primary patency rates ranging from 51% to 85%. The increased variation in inclusion criteria, length, and complexity of lesions between studies does not allow direct comparison between them. Larger randomized trials in specific patient populations comparing those modalities is needed before we can make safe recommendation of the superiority of one device over the other.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".